Biomedical subjects

Jean-Christophe Mozziconacci

Publications and source records attributed to Jean-Christophe Mozziconacci.

2 recordsLinked to original sources

PubMed2005

Optimization and validation of a docking-scoring protocol; application to virtual screening for COX-2 inhibitors.

To exploit available structural information about the cyclooxygenase enzyme for the virtual screening of large chemical libraries, a docking-scoring protocol was tuned and validated. The screening accuracy was assessed using a series of known inhibitors and a set of diverse a priori inactive compounds that was seeded with known active ligands. The major parameters of the DOCK algorithm were investigated. A large improvement of the results was obtained on tweaking some of them. The generated complexes were rescored using six scoring functions. In this way, the striking importance of this step was demonstrated, as well as the good performances of DOCK energy and SCORE for this target. The results were further improved via a consensus approach. As a first application, a subset of a large compound library was screened using this protocol. Among the compounds that were selected for biological testing, a third of them turned out to have a significant enzyme inhibition.

Crystallography, X-Ray↗

PubMedDate not supplied

2D QSAR consensus prediction for high-throughput virtual screening. An application to COX-2 inhibition modeling and screening of the NCI database.

Using classification (SOM, LVQ, Binary, Decision Tree) and regression algorithms (PLS, BRANN, k-NN, Linear), this paper details the building of eight 2D-QSAR models from a 266 COX-2 inhibitor training set. The predictive performances of these eight models were subsequently compared using an 88 COX-2 inhibitor test set. Each ligand is described by 52 2D descriptors expressed as van der Waals Surface Areas (P_VSA) and its COX-2 binding IC50. One of our best predictive models is the neural network model (BRANN), which is able to select a subset, from the 88 ligand test set, that contains 94% COX-2 active inhibitors (pIC50>7.5) and detects 71% of all the actives. We then introduce a QSAR consensus prediction protocol that is shown to be more predictive than any single QSAR model: our C3 consensus approach is able to select a subset from the 88 ligand test set that contains 94% active inhibitors and 83% of all the actives. The 2D QSAR consensus protocol was finally applied to the high-throughput virtual screening of the NCI database, containing 193,477 organic compounds.

Cyclooxygenase 2↗